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Brain Computer Interfaces as Stroke Rehabilitation Tools:
Optimization of current strategies
Arnau Espinosa1
Rupert Ortner1 Danut Irimia2
Christoph Guger1
1g.tec Guger Technologies OG 2Technical University of Iasi
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Brain-Computer-Interface (BCI)
“A system for controlling a device e.g. computer, wheelchair or a
neuroprothesis by human intention which does not depend on the
brain’s normal output pathways of peripheral nerves and muscles”
[Wolpaw et al., 2002].
HCI – Human Computer Interface
DBI – Direct Brain Interface (University of Michigan)
TTD – Thought Translation Device (University of Tübingen)
www.gtec.at
Subject/ Patient
Brain-Computer Interface
Device
Feedback
EEG/
ECoG control signal
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Activate a device
that assists movement train patients to produce
more normal brain activity
Daly, J. J. & Wolpaw, J. R. Brain-computer interfaces in neurological rehabilitation;
The Lancet Neurology, 2008, 7, 1032-1043
BCIs to help stroke survivors
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Motor imagery (MI) based rehabilitation was proven to be an
effective therapy.
Andrea Zimmermann-Schlatter, Corina Schuster, Milo A
Puhan, Ewa Siekierka and Johann Steurer. Efficacy of
motor imagery in post-stroke rehabilitation: a
systematic review; Journal of NeuroEngineering and
Rehabilitation
Stroke Rehabilitation
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Neurological rehabilitation via robotic devices shows promising
results in clinical trials.
Stroke Rehabilitation
Video
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The logical next step combines the two approaches into an
integrative rehabilitation strategy.
Stroke Rehabilitation
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www.gtec.at
Close the feedback loop and induce
“Hebbian plasticity”
“Cells that fire together, wire together.”
How to induce brain plasticity?
?
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Imagination of hand movement causes an ERD which is used to classify
the side of movement. The desynchronization occurs in motor and related
areas of the brain. Therefore, for analyzing and classifying ERD-patterns
the electrodes must be placed close to sensorimotor areas.
Left hand
movement Right hand
movement
C4
GND
REF
RIGHT
C3
Physiological Background – why does it work
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left
right
Fixation cross CUE
0 2 5 6
beep
7 4 3 1
motor imagery
8 s
Offline data classification
Recording of 40 trials minimum TRAINING
Paradigm for motor imagery BCI experiment
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left
right
Fixation cross CUE
0 2 5 6
beep
7 4 3 1
FB
8 s
feedback (FB)
classifier
FEEDBACK
Paradigm for motor imagery BCI experiment
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www.gtec.at
• Common Spatial Patterns weight each electrode according to the
importance to the discrimination task.
• The difference between left and right population is maximized.
• CSPs reflect the EEG source distribution.
• Setup of 4 CSPs: influence of electrode montage, sensitive to artifacts.
• The spatial filter suppresses artifacts.
• Variance calculation of 1 second segments -> fast feedback.
Right/Left hand motor imagery with
Common Spatial Patterns - principle
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www.gtec.at
Right/Left hand motor imagery with Common
Spatial Patterns: live experiment
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Error rate from the two feedback runs for S1. The vertical bar indicates the cue onset.
Classification expected
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Error rate from the two feedback runs for S1. The vertical bar indicates the cue onset.
Mean
Comparison bar-FB and VR-FB
Min
S1
S1
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Test of a generic set of Common Spatial Patterns (CSP) and
Linear Discriminant Analysis (LDA), for Motor Imagery (MI) -
Brain-Computer Interfaces with stroke patients.
Study
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• Eleven healthy subjects did EEG recordings with 64 EEG
channels.
• Users were instructed to imagine right or left hand movement
according to the arrow presented.
• All healthy test users performed one session, consisting of 80
trials.
• A general classifier and CSP feature vector was calculated using
data of this 11 subjects.
• The test was done over 11 healthy and 11 stroke patients.
• For check long term effect 5 stroke patients perform 4th more
sessions.
Methods:
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Results from 80 trials. The first number shows the mean error rate beginning from 3.5
seconds until 8 seconds. The number in parenthesis shows the minimum error rate within
this time.
Mean accuracy rates of the two groups
participating in the VR paradigm
Healthy Stroke
Session # 1 1 1 4
Participants 11 11 5 5
Mean Acc. 63.77 60.67 59.7 72.48
SD 16.52 13.05 6.08 8.45
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• Generic CSP and LDA classifier can be used for healthy persons
and also for stroke patients for MI training.
o Time is reduced -> keep motivation and ability of control
• Five stroke patients that participated to more training sessions,
increased their accuracy from 59,70% up to 72,48%.
• Difference accuracy between healthy users and stroke patients is
only about 3% on average.
Conclusions
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• Testing motor imagery in stroke patients.
• Study changes of ERD curves in stroke.
Krzeszowice Rehabilitation Center,
Poland
• Prove if stroke patients can control MI – BCI.
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If enough runs performed to divide data into test runs (for calculating
classifier and spatial filters) and test data, some patients able to achieve
very high accuracies.
Results, classifier same session
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1 (C3) 2 (C4)
3 (Cz)
Event Related Desynchronization / Synchronization
- relativ e power change
- signif icance
- ref erence
Legend:
Montagename:
...s_Laplacian_C3_C4_Cz.mat
Nr. trials: 37
Calc. f rom f ile:
...eate_Lap_part1_sav ed.mat
Av erage opt.:
[mean 16 samples]
Ty pe: BP
F.-borders: [8 12]
Realization: butter
Order: 4
Used signal:
Induced componets
Ref . int.:
[128 640] samples
With env elope: no
Parameter:
0 2 4 6 8-100
-50
0
50
100
time [s]
[%]
0 2 4 6 8-100
-50
0
50
100
time [s]
[%]
0 2 4 6 8-100
-50
0
50
100
time [s]
[%]
Right trials – good performer
? ERD in stroke survivors is significantly lower
Calculating ERD over time
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Left trials – good performer
1 (C3) 2 (C4)
3 (Cz)
Event Related Desynchronization / Synchronization
- relativ e power change
- signif icance
- ref erence
Legend:
Montagename:
...s_Laplacian_C3_C4_Cz.mat
Nr. trials: 34
Calc. f rom f ile:
...eate_Lap_part1_sav ed.mat
Av erage opt.:
[mean 16 samples]
Ty pe: BP
F.-borders: [8 12]
Realization: butter
Order: 4
Used signal:
Induced componets
Ref . int.:
[128 640] samples
With env elope: no
Parameter:
0 2 4 6 8-100
-50
0
50
100
time [s]
[%]
0 2 4 6 8-100
-50
0
50
100
time [s]
[%]
0 2 4 6 8-100
-50
0
50
100
time [s]
[%]
Calculating ERD over time
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Next Steps
g.REHAbci with robotic feedback
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www.gtec.at
Second Life Control
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www.gtec.at
University of Barcelona, Spain
Mel Slater,
Chris Groenegress
Bernhard Spanlang
IDIBAPS, Barcelona, Spain
Mavi Sanchez-Vives
Thomas Gener
University College London, UK
Anthony Steed
Angus Antely
Patrick Haggard
University of Technology Graz, Austria
Christa Neuper
Gernot Müller-Putz
Gert Pfurtscheller
Josef Faller
Wadsworth Center, New York, USA
Gerwin Schalk
Peter Brunner
Theresa Vaughan
Tel Aviv University, Israel
Matti Mintz
bDigital, Barcelona, Spain
Felip Miralles
University of Technology Munich,
Germany
Angelika Peer
Frank Wallhof
CNRS, France
Abder Kheddar
EPFL
Robert Leeb
Olaf Blanke
Nathan Evens
University of Pisa, Italy
Franco Tecchia
Massimo Bergamasco
IDC, Israel
Doron Friedman
INEX, Newcastle, UK
Angela Silmon
FSL, Rome, Italy
Fabio Aloise
Febo Cincotti
University of Würzburg
Andrea Kübler
University of Tübingen
Boris Kotchobey
University of Cambridge
Adrian Owen
Martin Monti
University of Washington
Kai Miller